Code Plateau Fellowship; Data Science Track Competition

Context:

The Tanzanian tourism sector plays a significant role in the Tanzanian economy, contributing about 17% to the country’s GDP and 25% of all foreign exchange revenues. The sector, which provides direct employment for more than 600,000 people and up to 2 million people indirectly, generated approximately $2.4 billion in 2018 according to government statistics. Tanzania received a record 1.1 million international visitor arrivals in 2014, mostly from Europe, the US and Africa. Tanzania is the only country in the world which has allocated more than 25% of its total area for wildlife, national parks, and protected areas.There are 16 national parks in Tanzania, 28 game reserves, 44 game-controlled areas, two marine parks and one conservation area.

Objective:

The objective of this competition is to explore and build a linear regression model that will predict the spending behaivior of tourists visiting Tanzania.The model can be used by different tour operators and the Tanzania Tourism Board to automatically help tourists across the world estimate their expenditure before visiting Tanzania.

Data Description

The dataset describes 6476 rows of up-to-date information on tourist expenditure collected by the National Bureau of Statistics (NBS) in Tanzania.The dataset was collected to gain a better understanding of the status of the tourism sector and provide an instrument that will enable sector growth. The survey covers seven departure points, namely: Julius Nyerere International Airport, Kilimanjaro International Airport, Abeid Amani Karume International Airport, and the Namanga, Tunduma, Mtukula and Manyovu border points.

Importing necessary libraries and data

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
%matplotlib inline
tz = pd.read_csv('Train .csv')

tz.head(10)

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
0	tour_0	SWIZERLAND	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	13.0	0.0	Cash	No	Friendly People	674602.5
1	tour_10	UNITED KINGDOM	25-44	NaN	1.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	14.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	3214906.5
2	tour_1000	UNITED KINGDOM	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	1.0	31.0	Cash	No	Excellent Experience	3315000.0
3	tour_1002	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	11.0	0.0	Cash	Yes	Friendly People	7790250.0
4	tour_1004	CHINA	1-24	NaN	1.0	0.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	7.0	4.0	Cash	Yes	No comments	1657500.0

5 rows × 23 columns

Data Overview

• Observations
• Sanity checks

tz.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 4809 entries, 0 to 4808
Data columns (total 23 columns):
 #   Column                 Non-Null Count  Dtype  
---  ------                 --------------  -----  
 0   ID                     4809 non-null   object 
 1   country                4809 non-null   object 
 2   age_group              4809 non-null   object 
 3   travel_with            3695 non-null   object 
 4   total_female           4806 non-null   float64
 5   total_male             4804 non-null   float64
 6   purpose                4809 non-null   object 
 7   main_activity          4809 non-null   object 
 8   info_source            4809 non-null   object 
 9   tour_arrangement       4809 non-null   object 
 10  package_transport_int  4809 non-null   object 
 11  package_accomodation   4809 non-null   object 
 12  package_food           4809 non-null   object 
 13  package_transport_tz   4809 non-null   object 
 14  package_sightseeing    4809 non-null   object 
 15  package_guided_tour    4809 non-null   object 
 16  package_insurance      4809 non-null   object 
 17  night_mainland         4809 non-null   float64
 18  night_zanzibar         4809 non-null   float64
 19  payment_mode           4809 non-null   object 
 20  first_trip_tz          4809 non-null   object 
 21  most_impressing        4496 non-null   object 
 22  total_cost             4809 non-null   float64
dtypes: float64(5), object(18)
memory usage: 864.2+ KB

tz.isnull().sum()

ID                          0
country                     0
age_group                   0
travel_with              1114
total_female                3
total_male                  5
purpose                     0
main_activity               0
info_source                 0
tour_arrangement            0
package_transport_int       0
package_accomodation        0
package_food                0
package_transport_tz        0
package_sightseeing         0
package_guided_tour         0
package_insurance           0
night_mainland              0
night_zanzibar              0
payment_mode                0
first_trip_tz               0
most_impressing           313
total_cost                  0
dtype: int64

There are four categories with missing values

sns.pairplot(tz, hue='country')

<seaborn.axisgrid.PairGrid at 0x11ef33692b0>

sns.distplot(tz['total_cost']).set(title = 'Spending distribution')

C:\Users\user\Downloads\New folder\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

[Text(0.5, 1.0, 'Spending distribution')]

sns.heatmap(tz.corr(), annot= True)

<AxesSubplot:>

Exploratory Data Analysis (EDA)

• EDA is an important part of any project involving data.
• It is important to investigate and understand the data better before building a model with it.
• A few questions have been mentioned below which will help you approach the analysis in the right manner and generate insights from the data.
• A thorough analysis of the data, in addition to the questions mentioned below, should be done.

Questions:

1. What are the top 5 countries with the highest spending statistic ?

2. which age-group are the highest spenders and who are the over all highest spenders by travel with?

3. which country are have the most spending tourists?

4. what is the average number of nights a toursits spends in Tanzania mainland?

5. what is the average number of nights a toursits spends in Zanzibar?

6. what is the most prefered payment mode by tourists?

7. Highlight the Aspects of tourism that are more profitable and in which it is worthwhile to invest in

8. what is the most sort after food by tourists?

#top 5 countries with highest spending statistics
countries = tz[['country', 'total_cost']].head(10).groupby('country').sum()
countries

	total_cost
country
CHINA	1657500.0
INDIA	2486250.0
NIGERIA	994500.0
SOUTH AFRICA	466140.0
SWIZERLAND	674602.5
UNITED KINGDOM	14441106.5
UNITED STATES OF AMERICA	3480750.0

plt.style.use('seaborn')
countries.plot(color='black', title= 'TOP 7 COUNTRIES', kind='bar')

<AxesSubplot:title={'center':'TOP 7 COUNTRIES'}, xlabel='country'>

#Age group with the highest spenders
Age = tz[['age_group', 'total_cost', 'travel_with']]
Age

	age_group	total_cost	travel_with
0	45-64	674602.5	Friends/Relatives
1	25-44	3214906.5	NaN
2	25-44	3315000.0	Alone
3	25-44	7790250.0	Spouse
4	1-24	1657500.0	NaN
...	...	...	...
4804	45-64	3315000.0	Alone
4805	25-44	10690875.0	Spouse
4806	1-24	2246636.7	NaN
4807	25-44	1160250.0	Friends/Relatives
4808	25-44	13260000.0	Spouse

4809 rows × 3 columns

Age_group = Age.groupby('age_group').sum()
Age_group

	total_cost
age_group
1-24	3.379088e+09
25-44	1.498710e+10
45-64	1.537184e+10
65+	5.284068e+09

Age_group.plot(kind='bar', title='HIGHEST SPENDING AGE GROUP')

<AxesSubplot:title={'center':'HIGHEST SPENDING AGE GROUP'}, xlabel='age_group'>

Age_count = Age[['age_group']].value_counts()
Age_count

age_group
25-44        2487
45-64        1391
1-24          624
65+           307
dtype: int64

#Country with the most spending tourist
cst =tz[['country', 'total_cost']].head(10)
cst

	country	total_cost
0	SWIZERLAND	674602.5
1	UNITED KINGDOM	3214906.5
2	UNITED KINGDOM	3315000.0
3	UNITED KINGDOM	7790250.0
4	CHINA	1657500.0
5	UNITED KINGDOM	120950.0
6	SOUTH AFRICA	466140.0
7	UNITED STATES OF AMERICA	3480750.0
8	NIGERIA	994500.0
9	INDIA	2486250.0

cst.plot(kind = 'bar', title = 'Country with the most spending tourist')

<AxesSubplot:title={'center':'Country with the most spending tourist'}>

#Average number of nights tourists spend at Tanzania Mainland
tz[['night_mainland']].mean()

night_mainland    8.488043
dtype: float64

An average of eight(8) nights are spent by tourists on Tanzania mainland

#Average number of nights tourists spend at Tanzania Mainland
tz[['night_zanzibar']].mean()

night_zanzibar    2.304429
dtype: float64

An average of two(2) nights are spent by tourists in Zanzibar

#Most preferred mode of payment by tourists
sns.catplot(data=tz, x= 'payment_mode', kind='count')

<seaborn.axisgrid.FacetGrid at 0x11eecff2400>

The most preferred mode of payment by the tourists is cash

#Aspects of tourism that are more profitable and in which it is worthwhile to invest in
sns.catplot(data= tz, y= 'main_activity', kind='count')

<seaborn.axisgrid.FacetGrid at 0x11eece7ac40>

The aspect of tourism that is more profitable and in which it is worthwhile to invest in is Wildlife tourism

#The most sort after food
sns.catplot(data=tz, x='package_food', kind='count' )

<seaborn.axisgrid.FacetGrid at 0x11eece7a040>

The most sort after food by the tourists is non package_food

Data Preprocessing

• Missing value treatment (if needed)
• Feature engineering
• Outlier detection and treatment (if needed)
• Preparing data for modeling
• Feature importance
• scaling

#Missing values in travel_with
tz['travel_with'].fillna(value='Alone', inplace= True)

tz

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
0	tour_0	SWIZERLAND	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	13.0	0.0	Cash	No	Friendly People	674602.5
1	tour_10	UNITED KINGDOM	25-44	Alone	1.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	14.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	3214906.5
2	tour_1000	UNITED KINGDOM	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	1.0	31.0	Cash	No	Excellent Experience	3315000.0
3	tour_1002	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	11.0	0.0	Cash	Yes	Friendly People	7790250.0
4	tour_1004	CHINA	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	7.0	4.0	Cash	Yes	No comments	1657500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4804	tour_993	UAE	45-64	Alone	0.0	1.0	Business	Hunting tourism	Friends, relatives	Independent	...	No	No	No	No	2.0	0.0	Credit Card	No	No comments	3315000.0
4805	tour_994	UNITED STATES OF AMERICA	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	11.0	0.0	Cash	Yes	Friendly People	10690875.0
4806	tour_995	NETHERLANDS	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	others	Independent	...	No	No	No	No	3.0	7.0	Cash	Yes	Good service	2246636.7
4807	tour_997	SOUTH AFRICA	25-44	Friends/Relatives	1.0	1.0	Business	Beach tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	5.0	0.0	Credit Card	No	Friendly People	1160250.0
4808	tour_999	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	4.0	7.0	Cash	Yes	Friendly People	13260000.0

4809 rows × 23 columns

#Missing values for total_male
tz['total_male'].fillna(method= 'bfill', inplace = True)

tz

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
0	tour_0	SWIZERLAND	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	13.0	0.0	Cash	No	Friendly People	674602.5
1	tour_10	UNITED KINGDOM	25-44	Alone	1.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	14.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	3214906.5
2	tour_1000	UNITED KINGDOM	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	1.0	31.0	Cash	No	Excellent Experience	3315000.0
3	tour_1002	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	11.0	0.0	Cash	Yes	Friendly People	7790250.0
4	tour_1004	CHINA	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	7.0	4.0	Cash	Yes	No comments	1657500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4804	tour_993	UAE	45-64	Alone	0.0	1.0	Business	Hunting tourism	Friends, relatives	Independent	...	No	No	No	No	2.0	0.0	Credit Card	No	No comments	3315000.0
4805	tour_994	UNITED STATES OF AMERICA	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	11.0	0.0	Cash	Yes	Friendly People	10690875.0
4806	tour_995	NETHERLANDS	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	others	Independent	...	No	No	No	No	3.0	7.0	Cash	Yes	Good service	2246636.7
4807	tour_997	SOUTH AFRICA	25-44	Friends/Relatives	1.0	1.0	Business	Beach tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	5.0	0.0	Credit Card	No	Friendly People	1160250.0
4808	tour_999	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	4.0	7.0	Cash	Yes	Friendly People	13260000.0

4809 rows × 23 columns

#Missing values for total_female
tz['total_female'].fillna(method='bfill', inplace = True)

tz

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
0	tour_0	SWIZERLAND	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	13.0	0.0	Cash	No	Friendly People	674602.5
1	tour_10	UNITED KINGDOM	25-44	Alone	1.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	14.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	3214906.5
2	tour_1000	UNITED KINGDOM	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	1.0	31.0	Cash	No	Excellent Experience	3315000.0
3	tour_1002	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	11.0	0.0	Cash	Yes	Friendly People	7790250.0
4	tour_1004	CHINA	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	7.0	4.0	Cash	Yes	No comments	1657500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4804	tour_993	UAE	45-64	Alone	0.0	1.0	Business	Hunting tourism	Friends, relatives	Independent	...	No	No	No	No	2.0	0.0	Credit Card	No	No comments	3315000.0
4805	tour_994	UNITED STATES OF AMERICA	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	11.0	0.0	Cash	Yes	Friendly People	10690875.0
4806	tour_995	NETHERLANDS	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	others	Independent	...	No	No	No	No	3.0	7.0	Cash	Yes	Good service	2246636.7
4807	tour_997	SOUTH AFRICA	25-44	Friends/Relatives	1.0	1.0	Business	Beach tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	5.0	0.0	Credit Card	No	Friendly People	1160250.0
4808	tour_999	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	4.0	7.0	Cash	Yes	Friendly People	13260000.0

4809 rows × 23 columns

#Missing values for most_impressing
tz['most_impressing'].fillna(value='no comments', inplace= True)

tz

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing	total_cost
0	tour_0	SWIZERLAND	45-64	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Independent	...	No	No	No	No	13.0	0.0	Cash	No	Friendly People	674602.5
1	tour_10	UNITED KINGDOM	25-44	Alone	1.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	14.0	7.0	Cash	Yes	Wonderful Country, Landscape, Nature	3214906.5
2	tour_1000	UNITED KINGDOM	25-44	Alone	0.0	1.0	Visiting Friends and Relatives	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	1.0	31.0	Cash	No	Excellent Experience	3315000.0
3	tour_1002	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	11.0	0.0	Cash	Yes	Friendly People	7790250.0
4	tour_1004	CHINA	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	7.0	4.0	Cash	Yes	No comments	1657500.0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
4804	tour_993	UAE	45-64	Alone	0.0	1.0	Business	Hunting tourism	Friends, relatives	Independent	...	No	No	No	No	2.0	0.0	Credit Card	No	No comments	3315000.0
4805	tour_994	UNITED STATES OF AMERICA	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	11.0	0.0	Cash	Yes	Friendly People	10690875.0
4806	tour_995	NETHERLANDS	1-24	Alone	1.0	0.0	Leisure and Holidays	Wildlife tourism	others	Independent	...	No	No	No	No	3.0	7.0	Cash	Yes	Good service	2246636.7
4807	tour_997	SOUTH AFRICA	25-44	Friends/Relatives	1.0	1.0	Business	Beach tourism	Travel, agent, tour operator	Independent	...	No	No	No	No	5.0	0.0	Credit Card	No	Friendly People	1160250.0
4808	tour_999	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	No	4.0	7.0	Cash	Yes	Friendly People	13260000.0

4809 rows × 23 columns

from pandas_profiling import ProfileReport
profile = ProfileReport(tz)
profile

Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]

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EDA

• It is a good idea to explore the data once again after manipulating it.

PREPARING DATA FOR MODELLING

#features for modelling
x = tz[['total_female',
       'total_male','night_mainland','night_zanzibar'
       ]]

#Target variable or Prediction variable.
y = tz[['total_cost']]

#Train test split
from sklearn.model_selection import train_test_split

x_test,x_train, y_test,y_train = train_test_split(x,y, test_size=0.8, random_state=105)

Building a Regression Model

#Creating and Training the model
from sklearn.linear_model import LinearRegression

#Instantiate model
lm = LinearRegression()

lm.fit(x_train,y_train)

LinearRegression()

print(lm.intercept_)

[4283710.80386905]

lm.coef_

array([[2142343.47631708,  693100.12934699,   50978.34380813,
         362964.92797582]])

x_train.columns

Index(['total_female', 'total_male', 'night_mainland', 'night_zanzibar'], dtype='object')

Testing Linear Model Assumptions

prediction = lm.predict(x_test)
prediction

array([[ 5333659.33987294],
       [10016757.31202075],
       [ 9659908.90536385],
       [ 5180724.30844856],
       [16425606.4751851 ],
       [ 6680945.99922677],
       [ 5594667.5802325 ],
       [ 7190729.43730806],
       [ 5180724.30844856],
       [11621552.05534841],
       [13788160.6701879 ],
       [ 8189699.62950382],
       [ 5078767.6208323 ],
       [15762719.70750957],
       [ 7886445.93158824],
       [ 9398900.66500429],
       [ 6782902.68684303],
       [12962039.34868405],
       [ 9312476.22965833],
       [ 7323067.78476564],
       [12126271.06173319],
       [ 5435616.0274892 ],
       [ 9720302.98012336],
       [10209489.73423784],
       [ 9659908.90536385],
       [ 5486594.37129733],
       [12525565.87269938],
       [ 6167662.40475976],
       [ 8183784.05493818],
       [34597173.64556116],
       [ 5771867.75017929],
       [ 6353226.21603552],
       [ 9618346.2925071 ],
       [ 5741486.09033797],
       [ 9264114.25078339],
       [ 5690507.74652984],
       [ 6680945.99922677],
       [ 6383607.87587684],
       [ 6731924.3430349 ],
       [ 9105062.69804009],
       [15914120.75136554],
       [ 5078767.6208323 ],
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sns.distplot(y_test- prediction/10**6)

C:\Users\user\Downloads\New folder\lib\site-packages\seaborn\distributions.py:2619: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms).
  warnings.warn(msg, FutureWarning)

<AxesSubplot:ylabel='Density'>

#Looking at the Linear evaluation metrics we get:
from sklearn import metrics

#Average error of the model
metrics.mean_absolute_error(y_test,prediction)

7159079.5603594845

#Checkmating target errors by squaring
metrics.mean_squared_error(y_test,prediction)

114911704866686.25

np.sqrt(metrics.mean_squared_error(y_test,prediction))

10719687.722442582

r_squared = lm.score(x_test, prediction)
r_squared

1.0

from sklearn.metrics import r2_score

r2_score(y_test,prediction)

0.10424171803690241

from sklearn.svm import SVC

tz2= pd.read_csv('Test .csv')
tz2.head(20)

	ID	country	age_group	travel_with	total_female	total_male	purpose	main_activity	info_source	tour_arrangement	...	package_food	package_transport_tz	package_sightseeing	package_guided_tour	package_insurance	night_mainland	night_zanzibar	payment_mode	first_trip_tz	most_impressing
0	tour_1	AUSTRALIA	45-64	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	Yes	10	3	Cash	Yes	Wildlife
1	tour_100	SOUTH AFRICA	25-44	Friends/Relatives	0.0	4.0	Business	Wildlife tourism	Tanzania Mission Abroad	Package Tour	...	No	No	No	No	No	13	0	Cash	No	Wonderful Country, Landscape, Nature
2	tour_1001	GERMANY	25-44	Friends/Relatives	3.0	0.0	Leisure and Holidays	Beach tourism	Friends, relatives	Independent	...	No	No	No	No	No	7	14	Cash	No	No comments
3	tour_1006	CANADA	24-Jan	Friends/Relatives	2.0	0.0	Leisure and Holidays	Cultural tourism	others	Independent	...	No	No	No	No	No	0	4	Cash	Yes	Friendly People
4	tour_1009	UNITED KINGDOM	45-64	Friends/Relatives	2.0	2.0	Leisure and Holidays	Wildlife tourism	Friends, relatives	Package Tour	...	Yes	Yes	No	No	No	10	0	Cash	Yes	Friendly People
5	tour_1010	DENMARK	25-44	NaN	1.0	0.0	Volunteering	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	No	55	0	Cash	No	Good service
6	tour_1014	GERMANY	45-64	NaN	0.0	1.0	Meetings and Conference	Mountain climbing	Travel, agent, tour operator	Package Tour	...	Yes	Yes	No	Yes	No	9	0	Cash	Yes	Friendly People
7	tour_1015	RUSSIA	24-Jan	Alone	1.0	0.0	Visiting Friends and Relatives	Beach tourism	Friends, relatives	Independent	...	No	No	No	No	No	26	3	Cash	No	Friendly People
8	tour_1019	GERMANY	24-Jan	Alone	1.0	0.0	Leisure and Holidays	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	No	3	0	Cash	No	Friendly People
9	tour_1020	FRANCE	24-Jan	Friends/Relatives	1.0	1.0	Leisure and Holidays	Wildlife tourism	Newspaper, magazines,brochures	Independent	...	No	No	No	No	No	30	9	Cash	Yes	Friendly People
10	tour_1023	SPAIN	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	No	No	Yes	6	4	Cash	Yes	Wildlife
11	tour_1025	UNITED KINGDOM	25-44	Spouse and Children	1.0	2.0	Leisure and Holidays	Wildlife tourism	Radio, TV, Web	Package Tour	...	Yes	Yes	Yes	Yes	No	6	6	Cash	Yes	Friendly People
12	tour_1031	SWIZERLAND	45-64	Spouse and Children	4.0	1.0	Leisure and Holidays	Wildlife tourism	Travel, agent, tour operator	Package Tour	...	Yes	Yes	Yes	Yes	No	10	7	Cash	Yes	Wildlife
13	tour_1037	UNITED STATES OF AMERICA	24-Jan	Friends/Relatives	1.0	1.0	Scientific and Academic	Cultural tourism	Friends, relatives	Independent	...	No	No	No	No	No	30	0	Cash	No	No comments
14	tour_1044	GERMANY	25-44	Alone	0.0	1.0	Leisure and Holidays	business	Friends, relatives	Package Tour	...	Yes	Yes	No	No	No	6	0	Cash	Yes	No comments
15	tour_1053	CANADA	45-64	Spouse and Children	4.0	1.0	Visiting Friends and Relatives	Wildlife tourism	Radio, TV, Web	Independent	...	No	No	No	No	No	21	0	Cash	No	Friendly People
16	tour_1056	SPAIN	25-44	Friends/Relatives	0.0	2.0	Leisure and Holidays	Wildlife tourism	Newspaper, magazines,brochures	Independent	...	No	No	No	No	No	10	6	Cash	Yes	Wonderful Country, Landscape, Nature
17	tour_106	CHINA	25-44	Friends/Relatives	2.0	2.0	Visiting Friends and Relatives	Mountain climbing	Trade fair	Package Tour	...	Yes	Yes	No	No	No	7	0	Cash	No	No comments
18	tour_1064	UNITED KINGDOM	25-44	Spouse and Children	4.0	1.0	Visiting Friends and Relatives	Beach tourism	Friends, relatives	Independent	...	No	No	No	No	No	30	0	Cash	No	Wonderful Country, Landscape, Nature
19	tour_1069	UNITED KINGDOM	25-44	Spouse	1.0	1.0	Leisure and Holidays	Wildlife tourism	Radio, TV, Web	Package Tour	...	No	Yes	Yes	Yes	No	6	7	Cash	Yes	Wildlife

20 rows × 22 columns

tz2.corr()

	total_female	total_male	night_mainland	night_zanzibar
total_female	1.000000	0.288933	0.015265	0.078020
total_male	0.288933	1.000000	-0.035880	0.020622
night_mainland	0.015265	-0.035880	1.000000	0.516262
night_zanzibar	0.078020	0.020622	0.516262	1.000000

sns.heatmap(tz2.corr(), annot= True)

<AxesSubplot:>

lm.fit(x,y)

LinearRegression()

y_pred = lm.predict(x_test)
y_pred

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Actionable Insights and Recommendations

**-After deep analysis, we observed that the most preferred mode of payment by the tourists is cash. We recommend that the Government of Tanzania should create a limit for the possession of cash on one's self, to avoid instances of robbery and pick-pocketing.

**We also observed that the aspects of tourism that are more profitable and in which it is worthwhile to invest in are Wildlife tourism, Beach tourism & Hunting tourism. We recommend that a lot of attention and effort, should be put into Diving and sport fishing and the other less profitable tourist attractions, to also make them worthwhile for investments and tourism